Open AccessPenentuan Tingkat Kerawanan Penyebaran Leptospirosis Menggunakan Inferensi Fuzzy Tsukamoto
Author(s) -
Ariesta Damayanti
Publication year - 2019
Publication title -
jurnal sistem komputer dan informatika (json)
Language(s) - English
Resource type - Journals
ISSN - 2685-998X
DOI - 10.30865/json.v1i1.1388
Subject(s) - leptospirosis , outbreak , geography , fuzzy inference , vulnerability (computing) , environmental health , computer science , fuzzy logic , medicine , artificial intelligence , computer security , veterinary medicine , virology , adaptive neuro fuzzy inference system , fuzzy control system
Cases of leptospirosis in Indonesia mainly occur in areas that often experience floods and areas where the majority of its citizens work as farmers. Special Region of Yogyakarta (DIY) was the province with the most leptospirosis cases in Indonesia in 2011. In 2010-2011 an extraordinary event (KLB) of leptospirosis occurred in Bantul district and in 2014 the number of leptospirosis cases in Bantul district increased by 76 cases.. Based on Kementerian Kesehatan report, data shows that there has been an outbreak of leptospirosis in Bantul , so in addition to epidemiological data necessary case information is also needed to determine the geographic case risk factors and mitigation efforts.In the processing of digital maps for GIS , often found important objects that are not appropriate in its processing can not even be excluded because of uncertainty owned. Applications are made in this study was built and designed by the architectural Tsukamoto fuzzy inference method for handling uncertainty. The results of the application is the visualization of the spread of the disease leptospirosis vulnerability maps based determinants that also involves uncertainty factors that will be resolved with the Tsukamoto fuzzy inference method for use as detection and prevention against the spread of disease leptospirosis in the future